Computer analysis of human errors in chess playing

Abstract

In this thesis we carried out a computer analysis of chess games played by a number of human players. We evaluated each move in these games and tried to find out when players are likely to make mistakes and when not. We tried to induce understandable error prediction rules with a rule learning program. In the analysis we generated discrete attributes for each position and its evaluation. These attributes describe chess positions and moves analysed. The analysis included a χ² test of the dependency between the attributes and human error and rule induction with the CN2 algorithm with EVC (Extreme Value Correction). With the χ² test we tried to determine which individual attributes were associated with errors. By generating rules we tried to find combinations of several attributes associated with errors. Using these two methods we tried to find the main characteristics of a player or groups of players. We analysed game data from two chess grandmasters - once world chess champions to try and determine the characteristics of individual players. We also analysed data from two groups of players which differed in the players ELO ratings. Here we tried to determine the characteristics of groups of players of different strengths. The results show that an analysis with the χ² test and rule generation can show that some characteristics of a chess position can indicate that certain players are more likely to make a good or a bad move in that position. Rule generation confirmed the results of the χ² test. Both methods yielded similar results regarding the characteristics of players. The results we generated were logical and could be interpreted. One of our more interesting conclusions was that players make fewer bad moves when they have compensation for having less material.